With the rapid development of artificial intelligence technology,optoelectronic neuromorphic devices with energy-efficient intelligent sensing and computation have received widespread attention.In order to enhance the universality of neuromorphic devices for different application scenarios,it is crucial to construct neuromorphic devices and circuits whose synaptic plasticity can be flexibly modulated.In this paper,we design an organic synaptic transistor based on an asymmetric electrode structure and utilize a large-area organic ultrathin semiconductor prepared in the liquid phase as a material for photoreception and computation.The photoelectric synaptic transistor exhibits typical excitatory postsynaptic potential(EPSC),paired-pulse facilitation(PPF),and spike-amplitude-dependent plasticity(SADP),which can achieve energy-efficient image noise reduction.To further satisfy the requirements of reservoir computing for synaptic plasticity tunability and network nonlinearity,we prepare organic n-type transistors and design a synaptic analogue circuit based on p-type and n-type transistors.The synaptic circuit achieves a high degree of tunability from short-time plasticity to long-time plasticity,as well as a configurable PPF that significantly enhances the current nonlinearity of the synaptic transistor.Based on the organic synaptic analogue circuits,we further construct a noise-reducing preprocessing fusion reservoir computing system,which is a reservoir neural network that exhibits 85％recognition accuracy for denoised MNIST handwritten datasets.This work demonstrates that the effective noise reduction function of organic photoelectric synaptic transistors and the flexible modulation capability of synaptic analogue circuits are important for realizing multimodal,multiscale,high-accuracy and low-power reservoir computing.
关键词
有机突触晶体管/突触模拟电路/高可调性/PPF/光电多模态储池计算
Key words
organic synaptic transistors/synaptic analog circuits/tunable synaptic plasticity/PPF/reservoir computing
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